Tuberculosis (TB) continues to be an important public health problem with 8.6 million new TB cases and 1.3 million TB deaths in 2012. For effective disease control, it is essential to know when and where disease occurs. Interventions targeted to areas with high rates of ongoing transmission are attractive to National Departments of Health, especially in high disease burden districts, but it is uncertain how to best achieve this. We hypothesize that the real-time identification of geographical areas with high ongoing transmission of TB and drug resistant TB can be accomplished by combining the Xpert MTB/RIF (Xpert) assay cycle threshold (Ct) value and geographic information systems (GIS) analytic techniques. The Xpert Ct value, which is routinely generated by the assay software, is a measure of Mycobacterium tuberculosis bacterial burden and could replace smear microscopy status as a marker of infectiousness, especially in high HIV burden settings. Similar to the community viral load for HIV, which allows identification of high HIV risk areas, we postulate that community mycobacterial load, defined as the median Ct value for a clinic over a specified period, identifies areas at high risk of ongoing TB transmission. We further hypothesize that the spatio-temporal distribution of hybridization drop out and delay of the five rpoB probe, the markers of rifampicin resistance on the Xpert assay, could be a novel approach to the molecular epidemiology of MDR-TB. Using the geo-spatial changes in probe distribution could be a feasible and highly effective method for real-time surveillance of MDR-TB outbreaks in high burden setting. South Africa is uniquely placed to test these hypotheses as South Africa is the first country to nationally replace smear microscopy by Xpert. To date, over 2.8 million Xpert assays have been performed in South Africa. All Xpert instruments are remotely connected and send test results to a centralized data warehouse in real time, allowing real-time access to results of the entire nation. In the proposed R21 study, we will aim to develop the optimal GIS methodology to generate spatial maps of ongoing transmission of M. tuberculosis by mapping the community mycobacterial load, and generate spatial maps of core areas of drug resistant TB and outbreaks of rifampicin resistant TB (aim 1). We will also identify patient, clinic, neighborhood and structural factors associated with hotspots of TB transmission to develop novel targeted interventions for TB control. The geospatial methodology developed and evidence gathered through this R21 to be performed in one of the nine provinces in South Africa, will lay the ground work to apply the optimal model to the remainder 8 provinces for a national surveillance tool, and for future RO1 research which will focus on (1) real-time implementation of the innovative advanced surveillance of TB transmission at a national level, and (2) evaluation of novel, targeted structural interventions to help break the cycle of transmission of drug sensitive and drug resistant TB and move SA towards efficient TB control.